Ultrasound imaging is an attractive modality for numerous diagnostic procedures because of its non-invasive nature, relatively low cost, and lack of radiation exposure. Medical ultrasound images are typically produced by generating an ultrasonic sound wave traveling in a known direction and observing the echoes created when the sound wave bounces off of boundaries between regions of differing density in the body. For any given direction, the image pixels are generated by plotting a dot whose brightness is proportional to the echo's amplitude at a coordinate whose location is a function of the time after the short sound pulse is send in the direction of the scan line being measured.
In addition to providing images of the body, ultrasound may also be used for measuring the flow of blood. Measurements related to the velocity of blood at each point in the image are useful in the diagnosis of arterial disease and general blood flow. For example, after transplant surgery, blood flow measurements may be used to ascertain if the transplanted organ is properly functioning. Similarly, by measuring the turbulence associated with the blood flow, arterial blockages and certain heart valve abnormalities may be detected.
The velocity measurements may be determined by measuring the Doppler shift of the ultrasound pulse reflected from the moving blood. It can be shown that the velocity and variance of the velocity may be obtained from an autocorrelation function based on the measured ultrasound intensities. Denote the autocorrelation function by R(t,x), where x represents the depth along the scan line, and t represents the time at which the scan was taken. R(t,x) is typically computed by averaging N scans taken at successive time points in the same direction according to the relationship: ##EQU1## where the superscript "*" denotes the complex conjugate. Here, x represents the depth along the scan direction and z(t,x) represents the complex amplitude of the return echo from depth x. The complex function z(t,x) is obtained by modulating the return echo signal with sine and cosine functions to obtain the in phase and quadrature components of the echo signal.
In blood measurements, the phase of R(t,x) provides the velocity of the blood flow while the variance, .sigma..sup.2, in the velocity provides an estimation of the turbulence. The variance may be estimated from R(t,x), i.e., ##EQU2##
While ultrasound has a number of advantages over other measurement modalities, it suffers from noise problems that make the measurements difficult to interpret without some form of noise reduction. This noise results from noise in the receiver and from the individual sound scattering centers which are moving during the image acquisition process. While the averaging in the time domain shown in Eq. (1) helps to reduce the effects of this noise, the averaging by itself is often insufficient to provide the accuracy needed for velocity measurements.
Spatial averaging filters have been used to improve the SNR in ultrasound images. The averaging operation combines a number of adjacent measurements in x to generate one averaged signal. In normal imaging, as opposed to the flow measurements described above, the spatially averaged image, z'(t,x) is obtained by computing the weighted average of z(t,x) at a number of points around x. ##EQU3## Here, the w.sub.k are weights used in the averaging, and x.sub.k is the digitized depth for the scan. The specific averaging algorithm depends on the choice of weights and the number of points averaged. While larger numbers of points provide a smoother image and higher effective SNR, the resultant image is blurred. The degree of blurring increases with the number of points. Hence, the spatial averaging represents a tradeoff between spatial resolution and SNR.
The same procedure can, in principle, be applied to the autocorrelation function R(t,x) to obtain an average autocorrelation function, R'(t,x), i.e., ##EQU4## where, the superscript "*" again denotes the complex conjugate. Again, the specific averaging algorithm depends on the choice of weights and the number of points averaged. While larger numbers of points provide a higher effective SNR, the resolution of the resultant velocity measurement is decreased. The loss in resolution increases with the number of points. Hence, the spatial averaging of the autocorrelation function also represents a tradeoff between resolution and SNR.
Prior art filtering techniques attempt to find a compromise filter length that provides satisfactory results over the entire image. Unfortunately, in ultrasound imaging, the SNR varies markedly over the image; hence, any single filter configuration is less than optimum for all locations in the image. In general, the SNR decreases with depth of the echo generating structure in the body. Echoes from deep tissue boundaries have much poorer SNRs than echoes from tissue boundaries near the transducer and receiver. Hence, a filter that provides satisfactory SNRs for the deep tissue portions of the scan will, in general, result in unnecessary blurring of the near tissue portion of the scan.
Broadly, it is the object of the present invention to provide an improved noise reduction filtering system for ultrasound measurements.
It is another object of the present invention to provide an improved noise reduction system that provides improved SNR during velocity measurements.
It is a further object of the present invention to provide a noise reduction filter having a length and/or weights that change in response to the signal SNR.
These and other objects of the present invention will become apparent to those skilled in the art from the following detailed description of the invention and the accompanying drawings.